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Record W3025923416 · doi:10.1149/ma2020-01492742mtgabs

Macroporous Rgo Electrode Decorated with Co<sub>3</sub>O<sub>4</sub> Nanoparticles for Selective Catalytic Electro-Oxidation of Water

2020· article· en· W3025923416 on OpenAlexaff
Gabriele Capilli, Yiwen Chen, Thomas Szkopek, Marta Cerruti

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMcGill University
Fundersnot available
KeywordsCatalysisNanoparticleGrapheneMaterials scienceChemical engineeringOxideElectrolyteInorganic chemistryMembraneNanotechnologyElectrodeChemistryMetallurgy

Abstract

fetched live from OpenAlex

OBJECTIVES The electro-oxidation of H2O to O2 is a promising process to store energy into a green fuel. Its intrinsic high kinetic barrier requires the use of catalysts to lower the applied potential; moreover, it suffers the competition of side-reactions like Cl‒ oxidation to chlorine, an undesirable corrosive by-product. The high concentration of Cl‒ ions in seawater is one of the major obstacles which hinders the direct employment of such widely available resource as the electrolyte for O2 production (Adv. Mater., 2018, 30, 1707261). The benchmark electrocatalysts based on noble metals (Pt, IrO2) are not selective toward Cl‒, and their scarcity prevents their practical large-scale application. Earth-abundant oxides and (oxy)hydroxides of first-row transition metals (FeOOH, NiOOH, Co3O4) are robust and show even higher catalytic performances than Pt/IrO2. Unfortunately they are not selective against Cl‒ oxidation (ACS Catal., 2014, 4, 3701). In this work, we aim to suppress Cl‒ interference on heterogeneous catalysts through a carefully designed electrode architecture. We synthesize a free-standing, conducting, 3D macroporous reduced graphene oxide (rGO) composite with catalytic Co3O4 nanoparticles (NP) selectively deposited just on the internal walls of its closed, not interconnected pores (average diameter 100 µm). The pore walls act as membranes made of multiple stacked rGO nanosheets; the nanochannels between rGO layers have width < 1 nm which previous works have shown to be permeable to water and gases while preventing the diffusion of dissolved ions like Cl‒ (e.g. Nat. Mater., 2017, 16, 1198). Since the catalytic sites are accessible selectively to the substrates able to permeate through the rGO walls, the water electro-oxidation is expected to happen without suffering the competing Cl2 evolution. The most common method for synthesizing 3D rGO porous materials is through self-assembly of graphene oxide (GO) suspensions during hydrothermal reduction. However, this method leads to scaffolds with small open pores with diameters in the micrometer range (e.g. ACS Nano, 2010, 7, 4324). A macroporous closed structure, instead, allows for selectivity together with higher diffusion rates of the permeating species from the outer solution to the reaction sites. For this reason, in our strategy we synthesize the scaffolds starting from emulsions of hexane droplets in water stabilized by GO nanosheets as soft template for the macroporous structure. RESULTS We modify the amphiphilicity of GO by adding different amounts of surfactants to produce so-called high internal phase emulsions (HIPE) stabilized by GO. These are single phase emulsions characterized by a high amount of emulsified phase (Hex:H2O ratio > 0.75), and a long-term stability (several months). Then, we convert the HIPE in the final 3D scaffolds by triggering the self-assembly of GO sheets through a hydrothermal reduction with slow temperature ramp, at the end of which hexane has entirely evaporated due to its low boiling point. Through this simple process we are able to control the architecture of the rGO scaffolds without polymerizations or post-modifications. We can control the pore size by tuning the templating HIPE droplet size, which we show depends on the GO concentration, hexane to water ratio, hexane to volume ratio, and emulsifying method (time and power). Because of its superior structural homogeneity compared to the conventional diluted emulsions previously reported (J. Mater. Chem. A, 2015, 3, 4018), a HIPE brings several advantages to the final material, i.e. better mechanical properties, very high surface area, and lower electrical resistivity (2.6±1.3 kΩcm). This is also the first study which reports the addition of a metal-organic precursor (of Co3+ in this specific case), or metal oxide NP (specifically Co3O4) functionalized with a hydrophobic layer, in the apolar phase during the preparation of the emulsion template, and then exploits the immiscible nature of the apolar and water phases to selectively decorate with catalytic NP only the internal wall of the closed pores. This feature requires the formation of highly stable initial GO HIPE and cannot be achieved by any other strategy previously reported. SIGNIFICANCE OF THE WORK The rGO macroporous materials produced are currently under investigation as self-standing electrocatalysts for selective water oxidation with respect to chlorine evolution. We will then test these electrodes for water decontamination and as anodes in energy storage devices. While in this work we focus on Co3O4, our approach allows the encapsulation of any type of nanocatalyst (metal oxide/metal-based NP) inside the closed rGO macropores, just by changing the hydrophobic precursor. Such materials may exhibit electrocatalytic activity and selectivity towards a variety of substrates that can permeate through the rGO walls while suppressing any competitive reaction involving interfering species which are rejected by the rGO walls. Figure 1

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.211
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
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